Universities

Tuition Fee

Report

U.S. Education Statistics

Name: Vineet Jain
Student Number: 19210982
Programme: MCM1 (Data Analytics)
Module Code: CA682
Assignment Title: Data Visualisation
Submission Date: 13 Dec 2019
Module Coordinator: Dr Suzanne Little

Raw Data Source: https://catalog.data.gov/dataset/college-scorecard/resource/2a7f670e-0799-436a-9394-df0a9b3ba7c5 (Size: 2.65 GB)

Data Dictionary Source: https://www.kaggle.com/kaggle/college-scorecard#CollegeScorecardDataDictionary-09-12-2015.csv (Size: 483 KB)

Conclusions

The map tells there are more universities in top 5 states of U.S than all the remaining states combined. Not only these states are top 5 in university count, but as per Wikipedia these are also the top 5 most populated states. There is obviously a direct correlation as more the universities, more the incoming students each year. Hawaii, a state with such small land area has almost the same university count as North Dakota and South Dakota and in fact more than double than Wyoming. As per the bar/column graph most degrees are being awarded in health professions and related courses (more than those in computing and business management combined), which raises the question – are there really that many jobs in healthcare industry? Lastly, we can see the increase in tuition fee over the past 17 years in the time series. If we look closely, we see the gap between in-state and out-of-state fees increasing with about 1500 USD difference in the early 2000s to 2000 USD in 2009 and 3000 USD in 2017.

---
title: "U.S University Statistics"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: flatly
    orientation: rows
    vertical_layout: fill
    
---

```{r setup, include=FALSE}


#install.packages(flexdashboard)
library(flexdashboard)
#install.packages(knitr)
library(knitr)
#install.packages(DT)
library(DT)
#install.packages(ggplot2)
library(ggplot2)
#install.packages(plotly)
library(plotly)
#install.packages(dplyr)
library(dplyr)
#install.packages(rpivotTable)
library(rpivotTable)
#install.packages(openintro)
library(openintro)
#install.packages(highcharter)
library(highcharter)
#install.packages(ggvis)
library(ggvis)
```


```{r}
setwd("/Users/Vineet PC/Downloads/R Dashboard/test 2")
temp = list.files(pattern="*.csv")
myfiles = lapply(temp, read.csv)
x=data.frame()
for(i in 1:length(myfiles)){
  myfiles[[i]]$YearOfData <- substr(temp[i], 7, 10)
  x=rbind(x,myfiles[[i]])
}

colnames(x)[1] <- c("UNITID")

x <- x[,c("UNITID",
          "INSTNM",
          "CITY",
          "STABBR",
          "ST_FIPS",
          "REGION",
          "ADM_RATE",
          "YearOfData",
          "ADM_RATE_ALL",
          "COMPL_RPY_1YR_RT",
          "PCIP01",
          "PCIP03",
          "PCIP04",
          "PCIP05",
          "PCIP09",
          "PCIP10",
          "PCIP11",
          "PCIP12",
          "PCIP13",
          "PCIP14",
          "PCIP15",
          "PCIP16",
          "PCIP19",
          "PCIP22",
          "PCIP23",
          "PCIP24",
          "PCIP25",
          "PCIP26",
          "PCIP27",
          "PCIP29",
          "PCIP30",
          "PCIP31",
          "PCIP38",
          "PCIP39",
          "PCIP40",
          "PCIP41",
          "PCIP42",
          "PCIP43",
          "PCIP44",
          "PCIP45",
          "PCIP46",
          "PCIP47",
          "PCIP48",
          "PCIP49",
          "PCIP50",
          "PCIP51",
          "PCIP52",
          "PCIP54",
          "TUITIONFEE_IN",
          "TUITIONFEE_OUT",
          "AVGFACSAL",
          "FEMALE",
          "MARRIED",
          "GRADS")]

# Replacing with NAs

x[x=="PrivacySuppressed"] <- NA
x[x=="NULL"] <- NA
x[x==""] <- NA


```

Universities
========================================

```{r}
aa <- x %>%
         group_by(STABBR) %>%
         summarize(total = n_distinct(UNITID))
aa$STABBR <- abbr2state(aa$STABBR)

h1 <- highchart() %>%
         hc_title(text = "Which states have the most universities?") %>%
         hc_add_series_map(usgeojson, aa,
                           name = "STABBR",
                           value = "total",
                           joinBy = c("woename", "STABBR")) %>%
         hc_mapNavigation(enabled = T) %>%
  hc_add_theme(hc_theme_chalk())

h1
```


Popular Degrees
=====================================

```{r}
aa <- x %>%
  group_by(STABBR) %>%
  summarize(total = n_distinct(UNITID))
aa$STABBR <- abbr2state(aa$STABBR)

aa <- aa %>% top_n(5)
ap <- x %>% filter(STABBR %in% state2abbr(aa$STABBR)) %>% select(c("PCIP01",
                     "PCIP03",
                     "PCIP04",
                     "PCIP05",
                     "PCIP09",
                     "PCIP10",
                     "PCIP11",
                     "PCIP12",
                     "PCIP13",
                     "PCIP14",
                     "PCIP15",
                     "PCIP16",
                     "PCIP19",
                     "PCIP22",
                     "PCIP23",
                     "PCIP24",
                     "PCIP25",
                     "PCIP26",
                     "PCIP27",
                     "PCIP29",
                     "PCIP30",
                     "PCIP31",
                     "PCIP38",
                     "PCIP39",
                     "PCIP40",
                     "PCIP41",
                     "PCIP42",
                     "PCIP43",
                     "PCIP44",
                     "PCIP45",
                     "PCIP46",
                     "PCIP47",
                     "PCIP48",
                     "PCIP49",
                     "PCIP50",
                     "PCIP51",
                     "PCIP52",
                     "PCIP54"))
for(i in 1:length(ap)){
  ap[i] <- as.numeric(as.vector(unlist(ap[i])))
  ap[i] <- ap[i]*100
}
ap2 <- data.frame(ap %>% summarise_each(funs(mean(., na.rm = TRUE))))

lis2 <- c(as.numeric(ap2[1,]))
lis2 <- round(lis2,2)

lis1 <- c("Agriculture, Agriculture Operations, And Related Sciences.",
          "Natural Resources And Conservation.",
          "Architecture And Related Services.",
          "Area, Ethnic, Cultural, Gender, And Group Studies.",
          "Communication, Journalism, And Related Programs.",
          "Communications Technologies/Technicians And Support Services.",
          "Computer And Information Sciences And Support Services.",
          "Personal And Culinary Services.",
          "Education.",
          "Engineering.",
          "Engineering Technologies And Engineering-Related Fields.",
          "Foreign Languages, Literatures, And Linguistics.",
          "Family And Consumer Sciences/Human Sciences.",
          "Legal Professions And Studies.",
          "English Language And Literature/Letters.",
          "Liberal Arts And Sciences, General Studies And Humanities.",
          "Library Science.",
          "Biological And Biomedical Sciences.",
          "Mathematics And Statistics.",
          "Military Technologies And Applied Sciences.",
          "Multi/Interdisciplinary Studies.",
          "Parks, Recreation, Leisure, And Fitness Studies.",
          "Philosophy And Religious Studies.",
          "Theology And Religious Vocations.",
          "Physical Sciences.",
          "Science Technologies/Technicians.",
          "Psychology.",
          "Homeland Security, Law Enforcement, Firefighting And Related Protective Services.",
          "Public Administration And Social Service Professions.",
          "Social Sciences.",
          "Construction Trades.",
          "Mechanic And Repair Technologies/Technicians.",
          "Precision Production.",
          "Transportation And Materials Moving.",
          "Visual And Performing Arts.",
          "Health Professions And Related Programs.",
          "Business, Management, Marketing, And Related Support Services.",
          "History.")

h2 <- highchart() %>% 
  hc_add_series(name="Percentage of Degrees Awarded",data=lis2, type = "column", color = "cyan") %>%
  hc_xAxis(categories = lis1) %>%
  hc_title(text = "What are the most popular courses in top states?") %>%
  hc_add_theme(hc_theme_chalk())

h2

```


Tuition Fee
========================================

```{r}
aa <- x %>%
  group_by(STABBR) %>%
  summarize(total = n_distinct(UNITID))
aa$STABBR <- abbr2state(aa$STABBR)
aa <- aa %>% top_n(5)

x$TUITIONFEE_IN <- as.numeric(as.vector(x$TUITIONFEE_IN))
x$TUITIONFEE_OUT <- as.numeric(as.vector(x$TUITIONFEE_OUT))


z <- x %>% filter(STABBR %in% state2abbr(aa$STABBR)) %>% select(c("YearOfData","TUITIONFEE_IN", "TUITIONFEE_OUT")) %>% group_by(YearOfData) %>% summarise_each(funs(mean(., na.rm = TRUE)))

z$TUITIONFEE_IN <- round(z$TUITIONFEE_IN,2)
z$TUITIONFEE_OUT <- round(z$TUITIONFEE_OUT,2)
z$Gap <- z$TUITIONFEE_OUT - z$TUITIONFEE_IN

h3 <- highchart() %>%
  hc_title(text = "How much has the Tuition Fee increased in these top states?") %>%
  hc_xAxis(categories = z$YearOfData) %>%
  hc_add_series(name = "Tuition Fee_IN", data = z$TUITIONFEE_IN, color = "lightgreen") %>%
  hc_add_series(name = "Tuition Fee_OUT", data = z$TUITIONFEE_OUT, color = "yellow") %>%
  hc_add_theme(hc_theme_chalk())

h3

```

Report
========================================

U.S. Education Statistics

Name:	Vineet Jain

Student Number:	19210982

Programme: MCM1 (Data Analytics)

Module Code:	CA682

Assignment Title:	Data Visualisation

Submission Date:	13 Dec 2019

Module Coordinator:	Dr Suzanne Little



Raw Data Source: https://catalog.data.gov/dataset/college-scorecard/resource/2a7f670e-0799-436a-9394-df0a9b3ba7c5
 (Size: 2.65 GB)

Data Dictionary Source: https://www.kaggle.com/kaggle/college-scorecard#CollegeScorecardDataDictionary-09-12-2015.csv 
 (Size: 483 KB)


Conclusions

The map tells there are more universities in top 5 states of U.S than all the remaining states combined. Not only these states are top 5 in university count, but as per Wikipedia these are also the top 5 most populated states. There is obviously a direct correlation as more the universities, more the incoming students each year. Hawaii, a state with such small land area has almost the same university count as North Dakota and South Dakota and in fact more than double than Wyoming.
As per the bar/column graph most degrees are being awarded in health professions and related courses (more than those in computing and business management combined), which raises the question – are there really that many jobs in healthcare industry?
Lastly, we can see the increase in tuition fee over the past 17 years in the time series. If we look closely, we see the gap between in-state and out-of-state fees increasing with about 1500 USD difference in the early 2000s to 2000 USD in 2009 and 3000 USD in 2017.